Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/20/2008
Publication Date: 8/15/2008
Publication URL: http://handle.nal.usda.gov/10113/22645
Citation: Crow, W.T., Reichle, R. 2008. Comparison of adaptive filtering techniques for land surface data assimilation. Water Resources Research. 44(W08423):100-112. Interpretive Summary: Data assimilation systems are powerful tools for optimally combining information gleaned from a variety of sources – including remote sensing data obtained from spaceborne sensors. Like any model, these systems require input parameters to describe the error characteristics of both model predictions and observations related to these predictions. This manuscript describes and compares various “adaptive filtering” techniques for obtaining such information during the operational implementation of a data assimilation system to merge remotely-sensed soil moisture observations into a hydrologic model. Results of this study can be used to enhance the integration of remote sensing measurements with models and improve the quality of soil moisture estimates used for weather prediction, drought monitoring, numerical weather prediction and irrigation scheduling applications.
Technical Abstract: The accurate specification of modeling and observational error information required by data assimilation algorithms is a major obstacle to the successful application of a land surface data assimilation system. The source and statistical structure of these errors are often unknown and poor assumptions concerning the relative magnitude of modeling and observation uncertainty degrade the quality of land data assimilation predictions. In theory, adaptive filtering approaches are capable of estimating model and observation error covariance information during the on-line cycling of a data assimilation system. To date, however, these approaches have not been widely applied to land surface models. Here, we implement and compare four separate adaptive filtering schemes in a data assimilation system designed to ingest remotely-sensed surface soil moisture retrievals. Upon testing of each scheme via a synthetic twin data assimilation experiment, three of the four adaptive approaches are found to provide substantially improved soil moisture estimates. However, the specific model and observation characteristics of satellite-based surface soil moisture retrievals contribute to the relatively slow convergence of all schemes. Overall, results highlight the need to consider unique aspects of the land data assimilation problem when designing and/or evaluating the relative performance of adaptive filtering algorithms.